bcc7d277f03a4405a1abdcc183d47bd5

PARKINSON'S DISEASE PREDICTION

Importing necessary libraries

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
import os

# Read the Excel file
df = pd.read_excel('/content/Parkinsson disease.xlsx')

# Display the dataframe
display(df)

	name	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
0	phon_R01_S01_1	119.992	157.302	74.997	0.00784	0.00007	0.00370	0.00554	0.01109	0.04374	...	0.06545	0.02211	21.033	1	0.414783	0.815285	-4.813031	0.266482	2.301442	0.284654
1	phon_R01_S01_2	122.400	148.650	113.819	0.00968	0.00008	0.00465	0.00696	0.01394	0.06134	...	0.09403	0.01929	19.085	1	0.458359	0.819521	-4.075192	0.335590	2.486855	0.368674
2	phon_R01_S01_3	116.682	131.111	111.555	0.01050	0.00009	0.00544	0.00781	0.01633	0.05233	...	0.08270	0.01309	20.651	1	0.429895	0.825288	-4.443179	0.311173	2.342259	0.332634
3	phon_R01_S01_4	116.676	137.871	111.366	0.00997	0.00009	0.00502	0.00698	0.01505	0.05492	...	0.08771	0.01353	20.644	1	0.434969	0.819235	-4.117501	0.334147	2.405554	0.368975
4	phon_R01_S01_5	116.014	141.781	110.655	0.01284	0.00011	0.00655	0.00908	0.01966	0.06425	...	0.10470	0.01767	19.649	1	0.417356	0.823484	-3.747787	0.234513	2.332180	0.410335
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
190	phon_R01_S50_2	174.188	230.978	94.261	0.00459	0.00003	0.00263	0.00259	0.00790	0.04087	...	0.07008	0.02764	19.517	0	0.448439	0.657899	-6.538586	0.121952	2.657476	0.133050
191	phon_R01_S50_3	209.516	253.017	89.488	0.00564	0.00003	0.00331	0.00292	0.00994	0.02751	...	0.04812	0.01810	19.147	0	0.431674	0.683244	-6.195325	0.129303	2.784312	0.168895
192	phon_R01_S50_4	174.688	240.005	74.287	0.01360	0.00008	0.00624	0.00564	0.01873	0.02308	...	0.03804	0.10715	17.883	0	0.407567	0.655683	-6.787197	0.158453	2.679772	0.131728
193	phon_R01_S50_5	198.764	396.961	74.904	0.00740	0.00004	0.00370	0.00390	0.01109	0.02296	...	0.03794	0.07223	19.020	0	0.451221	0.643956	-6.744577	0.207454	2.138608	0.123306
194	phon_R01_S50_6	214.289	260.277	77.973	0.00567	0.00003	0.00295	0.00317	0.00885	0.01884	...	0.03078	0.04398	21.209	0	0.462803	0.664357	-5.724056	0.190667	2.555477	0.148569

195 rows × 24 columns

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import ydata_profiling as pf
display(pf.ProfileReport(df))

{"model_id":"78b97d58cbba463fbaebf5019ae211b7","version_major":2,"version_minor":0}

{"model_id":"a77b7ead0d0c4d12a21192862f260590","version_major":2,"version_minor":0}

{"model_id":"2e1cd13353374337b4c9b9b0d59c01ba","version_major":2,"version_minor":0}

Displaying the shape

display (df.shape)

(195, 24)

No of rows

print (len(df))

displaying data types of columns

display (df.dtypes )

name                 object
MDVP:Fo(Hz)         float64
MDVP:Fhi(Hz)        float64
MDVP:Flo(Hz)        float64
MDVP:Jitter(%)      float64
MDVP:Jitter(Abs)    float64
MDVP:RAP            float64
MDVP:PPQ            float64
Jitter:DDP          float64
MDVP:Shimmer        float64
MDVP:Shimmer(dB)    float64
Shimmer:APQ3        float64
Shimmer:APQ5        float64
MDVP:APQ            float64
Shimmer:DDA         float64
NHR                 float64
HNR                 float64
status                int64
RPDE                float64
DFA                 float64
spread1             float64
spread2             float64
D2                  float64
PPE                 float64
dtype: object

print (df.info())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 195 entries, 0 to 194
Data columns (total 24 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   name              195 non-null    object 
 1   MDVP:Fo(Hz)       195 non-null    float64
 2   MDVP:Fhi(Hz)      195 non-null    float64
 3   MDVP:Flo(Hz)      195 non-null    float64
 4   MDVP:Jitter(%)    195 non-null    float64
 5   MDVP:Jitter(Abs)  195 non-null    float64
 6   MDVP:RAP          195 non-null    float64
 7   MDVP:PPQ          195 non-null    float64
 8   Jitter:DDP        195 non-null    float64
 9   MDVP:Shimmer      195 non-null    float64
 10  MDVP:Shimmer(dB)  195 non-null    float64
 11  Shimmer:APQ3      195 non-null    float64
 12  Shimmer:APQ5      195 non-null    float64
 13  MDVP:APQ          195 non-null    float64
 14  Shimmer:DDA       195 non-null    float64
 15  NHR               195 non-null    float64
 16  HNR               195 non-null    float64
 17  status            195 non-null    int64  
 18  RPDE              195 non-null    float64
 19  DFA               195 non-null    float64
 20  spread1           195 non-null    float64
 21  spread2           195 non-null    float64
 22  D2                195 non-null    float64
 23  PPE               195 non-null    float64
dtypes: float64(22), int64(1), object(1)
memory usage: 36.7+ KB
None

display (df.describe())

	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	MDVP:Shimmer(dB)	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
count	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	...	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000
mean	154.228641	197.104918	116.324631	0.006220	0.000044	0.003306	0.003446	0.009920	0.029709	0.282251	...	0.046993	0.024847	21.885974	0.753846	0.498536	0.718099	-5.684397	0.226510	2.381826	0.206552
std	41.390065	91.491548	43.521413	0.004848	0.000035	0.002968	0.002759	0.008903	0.018857	0.194877	...	0.030459	0.040418	4.425764	0.431878	0.103942	0.055336	1.090208	0.083406	0.382799	0.090119
min	88.333000	102.145000	65.476000	0.001680	0.000007	0.000680	0.000920	0.002040	0.009540	0.085000	...	0.013640	0.000650	8.441000	0.000000	0.256570	0.574282	-7.964984	0.006274	1.423287	0.044539
25%	117.572000	134.862500	84.291000	0.003460	0.000020	0.001660	0.001860	0.004985	0.016505	0.148500	...	0.024735	0.005925	19.198000	1.000000	0.421306	0.674758	-6.450096	0.174351	2.099125	0.137451
50%	148.790000	175.829000	104.315000	0.004940	0.000030	0.002500	0.002690	0.007490	0.022970	0.221000	...	0.038360	0.011660	22.085000	1.000000	0.495954	0.722254	-5.720868	0.218885	2.361532	0.194052
75%	182.769000	224.205500	140.018500	0.007365	0.000060	0.003835	0.003955	0.011505	0.037885	0.350000	...	0.060795	0.025640	25.075500	1.000000	0.587562	0.761881	-5.046192	0.279234	2.636456	0.252980
max	260.105000	592.030000	239.170000	0.033160	0.000260	0.021440	0.019580	0.064330	0.119080	1.302000	...	0.169420	0.314820	33.047000	1.000000	0.685151	0.825288	-2.434031	0.450493	3.671155	0.527367

8 rows × 23 columns

display (df.isna().sum() )

name                0
MDVP:Fo(Hz)         0
MDVP:Fhi(Hz)        0
MDVP:Flo(Hz)        0
MDVP:Jitter(%)      0
MDVP:Jitter(Abs)    0
MDVP:RAP            0
MDVP:PPQ            0
Jitter:DDP          0
MDVP:Shimmer        0
MDVP:Shimmer(dB)    0
Shimmer:APQ3        0
Shimmer:APQ5        0
MDVP:APQ            0
Shimmer:DDA         0
NHR                 0
HNR                 0
status              0
RPDE                0
DFA                 0
spread1             0
spread2             0
D2                  0
PPE                 0
dtype: int64

print (df.columns)

Index(['name', 'MDVP:Fo(Hz)', 'MDVP:Fhi(Hz)', 'MDVP:Flo(Hz)', 'MDVP:Jitter(%)',
       'MDVP:Jitter(Abs)', 'MDVP:RAP', 'MDVP:PPQ', 'Jitter:DDP',
       'MDVP:Shimmer', 'MDVP:Shimmer(dB)', 'Shimmer:APQ3', 'Shimmer:APQ5',
       'MDVP:APQ', 'Shimmer:DDA', 'NHR', 'HNR', 'status', 'RPDE', 'DFA',
       'spread1', 'spread2', 'D2', 'PPE'],
      dtype='object')

print (df['status'])

0      1
1      1
2      1
3      1
4      1
      ..
190    0
191    0
192    0
193    0
194    0
Name: status, Length: 195, dtype: int64

Histogram

# The dataset has high number of patients effected with Parkinson's disease.
plt.figure(figsize=(10, 6))
df.status.hist()
plt.xlabel('Status')
plt.ylabel('Frequencies')
plt.plot()
plt.show()

plt.figure(figsize=(10, 6))
sns.barplot(x="status",y="NHR",data=df);
plt.show()

plt.figure(figsize=(10, 6))
sns.barplot(x="status",y="HNR",data=df);
plt.show()

plt.figure(figsize=(10, 6))
sns.barplot(x="status",y="RPDE",data=df);
plt.show()

import warnings
warnings.filterwarnings('ignore')
rows=3
cols=7
fig, ax=plt.subplots(nrows=rows,ncols=cols,figsize=(16,4))
col=df.columns
index=1
for i in range(rows):
    for j in range(cols):
        sns.distplot(df[col[index]],ax=ax[i][j])
        index=index+1

plt.tight_layout()
plt.show()

display (df.head(3))

	name	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
0	phon_R01_S01_1	119.992	157.302	74.997	0.00784	0.00007	0.00370	0.00554	0.01109	0.04374	...	0.06545	0.02211	21.033	1	0.414783	0.815285	-4.813031	0.266482	2.301442	0.284654
1	phon_R01_S01_2	122.400	148.650	113.819	0.00968	0.00008	0.00465	0.00696	0.01394	0.06134	...	0.09403	0.01929	19.085	1	0.458359	0.819521	-4.075192	0.335590	2.486855	0.368674
2	phon_R01_S01_3	116.682	131.111	111.555	0.01050	0.00009	0.00544	0.00781	0.01633	0.05233	...	0.08270	0.01309	20.651	1	0.429895	0.825288	-4.443179	0.311173	2.342259	0.332634

3 rows × 24 columns

dfc=df.iloc[:,1:]
corr = dfc.corr()
display (corr)

	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	MDVP:Shimmer(dB)	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
MDVP:Fo(Hz)	1.000000	0.400985	0.596546	-0.118003	-0.382027	-0.076194	-0.112165	-0.076213	-0.098374	-0.073742	...	-0.094732	-0.021981	0.059144	-0.383535	-0.383894	-0.446013	-0.413738	-0.249450	0.177980	-0.372356
MDVP:Fhi(Hz)	0.400985	1.000000	0.084951	0.102086	-0.029198	0.097177	0.091126	0.097150	0.002281	0.043465	...	-0.003733	0.163766	-0.024893	-0.166136	-0.112404	-0.343097	-0.076658	-0.002954	0.176323	-0.069543
MDVP:Flo(Hz)	0.596546	0.084951	1.000000	-0.139919	-0.277815	-0.100519	-0.095828	-0.100488	-0.144543	-0.119089	...	-0.150737	-0.108670	0.210851	-0.380200	-0.400143	-0.050406	-0.394857	-0.243829	-0.100629	-0.340071
MDVP:Jitter(%)	-0.118003	0.102086	-0.139919	1.000000	0.935714	0.990276	0.974256	0.990276	0.769063	0.804289	...	0.746635	0.906959	-0.728165	0.278220	0.360673	0.098572	0.693577	0.385123	0.433434	0.721543
MDVP:Jitter(Abs)	-0.382027	-0.029198	-0.277815	0.935714	1.000000	0.922911	0.897778	0.922913	0.703322	0.716601	...	0.697170	0.834972	-0.656810	0.338653	0.441839	0.175036	0.735779	0.388543	0.310694	0.748162
MDVP:RAP	-0.076194	0.097177	-0.100519	0.990276	0.922911	1.000000	0.957317	1.000000	0.759581	0.790652	...	0.744919	0.919521	-0.721543	0.266668	0.342140	0.064083	0.648328	0.324407	0.426605	0.670999
MDVP:PPQ	-0.112165	0.091126	-0.095828	0.974256	0.897778	0.957317	1.000000	0.957319	0.797826	0.839239	...	0.763592	0.844604	-0.731510	0.288698	0.333274	0.196301	0.716489	0.407605	0.412524	0.769647
Jitter:DDP	-0.076213	0.097150	-0.100488	0.990276	0.922913	1.000000	0.957319	1.000000	0.759555	0.790621	...	0.744901	0.919548	-0.721494	0.266646	0.342079	0.064026	0.648328	0.324377	0.426556	0.671005
MDVP:Shimmer	-0.098374	0.002281	-0.144543	0.769063	0.703322	0.759581	0.797826	0.759555	1.000000	0.987258	...	0.987626	0.722194	-0.835271	0.367430	0.447424	0.159954	0.654734	0.452025	0.507088	0.693771
MDVP:Shimmer(dB)	-0.073742	0.043465	-0.119089	0.804289	0.716601	0.790652	0.839239	0.790621	0.987258	1.000000	...	0.963202	0.744477	-0.827805	0.350697	0.410684	0.165157	0.652547	0.454314	0.512233	0.695058
Shimmer:APQ3	-0.094717	-0.003743	-0.150747	0.746625	0.697153	0.744912	0.763580	0.744894	0.987625	0.963198	...	1.000000	0.716207	-0.827123	0.347617	0.435242	0.151124	0.610967	0.402243	0.467265	0.645377
Shimmer:APQ5	-0.070682	-0.009997	-0.101095	0.725561	0.648961	0.709927	0.786780	0.709907	0.982835	0.973751	...	0.960072	0.658080	-0.813753	0.351148	0.399903	0.213873	0.646809	0.457195	0.502174	0.702456
MDVP:APQ	-0.077774	0.004937	-0.107293	0.758255	0.648793	0.737455	0.804139	0.737439	0.950083	0.960977	...	0.896647	0.694019	-0.800407	0.364316	0.451379	0.157276	0.673158	0.502188	0.536869	0.721694
Shimmer:DDA	-0.094732	-0.003733	-0.150737	0.746635	0.697170	0.744919	0.763592	0.744901	0.987626	0.963202	...	1.000000	0.716215	-0.827130	0.347608	0.435237	0.151132	0.610971	0.402223	0.467261	0.645389
NHR	-0.021981	0.163766	-0.108670	0.906959	0.834972	0.919521	0.844604	0.919548	0.722194	0.744477	...	0.716215	1.000000	-0.714072	0.189429	0.370890	-0.131882	0.540865	0.318099	0.470949	0.552591
HNR	0.059144	-0.024893	0.210851	-0.728165	-0.656810	-0.721543	-0.731510	-0.721494	-0.835271	-0.827805	...	-0.827130	-0.714072	1.000000	-0.361515	-0.598736	-0.008665	-0.673210	-0.431564	-0.601401	-0.692876
status	-0.383535	-0.166136	-0.380200	0.278220	0.338653	0.266668	0.288698	0.266646	0.367430	0.350697	...	0.347608	0.189429	-0.361515	1.000000	0.308567	0.231739	0.564838	0.454842	0.340232	0.531039
RPDE	-0.383894	-0.112404	-0.400143	0.360673	0.441839	0.342140	0.333274	0.342079	0.447424	0.410684	...	0.435237	0.370890	-0.598736	0.308567	1.000000	-0.110950	0.591117	0.479905	0.236931	0.545886
DFA	-0.446013	-0.343097	-0.050406	0.098572	0.175036	0.064083	0.196301	0.064026	0.159954	0.165157	...	0.151132	-0.131882	-0.008665	0.231739	-0.110950	1.000000	0.195668	0.166548	-0.165381	0.270445
spread1	-0.413738	-0.076658	-0.394857	0.693577	0.735779	0.648328	0.716489	0.648328	0.654734	0.652547	...	0.610971	0.540865	-0.673210	0.564838	0.591117	0.195668	1.000000	0.652358	0.495123	0.962435
spread2	-0.249450	-0.002954	-0.243829	0.385123	0.388543	0.324407	0.407605	0.324377	0.452025	0.454314	...	0.402223	0.318099	-0.431564	0.454842	0.479905	0.166548	0.652358	1.000000	0.523532	0.644711
D2	0.177980	0.176323	-0.100629	0.433434	0.310694	0.426605	0.412524	0.426556	0.507088	0.512233	...	0.467261	0.470949	-0.601401	0.340232	0.236931	-0.165381	0.495123	0.523532	1.000000	0.480585
PPE	-0.372356	-0.069543	-0.340071	0.721543	0.748162	0.670999	0.769647	0.671005	0.693771	0.695058	...	0.645389	0.552591	-0.692876	0.531039	0.545886	0.270445	0.962435	0.644711	0.480585	1.000000

23 rows × 23 columns

from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 20,10
sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, cmap='cubehelix',annot = True)
plt.show()

from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 20,10
sns.heatmap(corr)
plt.show()

# Removing  name column for machine learning algorithms.
df.drop(['name'],axis=1,inplace=True)
display (df)

	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	MDVP:Shimmer(dB)	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
0	119.992	157.302	74.997	0.00784	0.00007	0.00370	0.00554	0.01109	0.04374	0.426	...	0.06545	0.02211	21.033	1	0.414783	0.815285	-4.813031	0.266482	2.301442	0.284654
1	122.400	148.650	113.819	0.00968	0.00008	0.00465	0.00696	0.01394	0.06134	0.626	...	0.09403	0.01929	19.085	1	0.458359	0.819521	-4.075192	0.335590	2.486855	0.368674
2	116.682	131.111	111.555	0.01050	0.00009	0.00544	0.00781	0.01633	0.05233	0.482	...	0.08270	0.01309	20.651	1	0.429895	0.825288	-4.443179	0.311173	2.342259	0.332634
3	116.676	137.871	111.366	0.00997	0.00009	0.00502	0.00698	0.01505	0.05492	0.517	...	0.08771	0.01353	20.644	1	0.434969	0.819235	-4.117501	0.334147	2.405554	0.368975
4	116.014	141.781	110.655	0.01284	0.00011	0.00655	0.00908	0.01966	0.06425	0.584	...	0.10470	0.01767	19.649	1	0.417356	0.823484	-3.747787	0.234513	2.332180	0.410335
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
190	174.188	230.978	94.261	0.00459	0.00003	0.00263	0.00259	0.00790	0.04087	0.405	...	0.07008	0.02764	19.517	0	0.448439	0.657899	-6.538586	0.121952	2.657476	0.133050
191	209.516	253.017	89.488	0.00564	0.00003	0.00331	0.00292	0.00994	0.02751	0.263	...	0.04812	0.01810	19.147	0	0.431674	0.683244	-6.195325	0.129303	2.784312	0.168895
192	174.688	240.005	74.287	0.01360	0.00008	0.00624	0.00564	0.01873	0.02308	0.256	...	0.03804	0.10715	17.883	0	0.407567	0.655683	-6.787197	0.158453	2.679772	0.131728
193	198.764	396.961	74.904	0.00740	0.00004	0.00370	0.00390	0.01109	0.02296	0.241	...	0.03794	0.07223	19.020	0	0.451221	0.643956	-6.744577	0.207454	2.138608	0.123306
194	214.289	260.277	77.973	0.00567	0.00003	0.00295	0.00317	0.00885	0.01884	0.190	...	0.03078	0.04398	21.209	0	0.462803	0.664357	-5.724056	0.190667	2.555477	0.148569

195 rows × 23 columns

#Spitting the dataset into x and y
#Create X
X=df.drop(labels=['status'],axis=1)
display (X.head())

	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	MDVP:Shimmer(dB)	...	MDVP:APQ	Shimmer:DDA	NHR	HNR	RPDE	DFA	spread1	spread2	D2	PPE
0	119.992	157.302	74.997	0.00784	0.00007	0.00370	0.00554	0.01109	0.04374	0.426	...	0.02971	0.06545	0.02211	21.033	0.414783	0.815285	-4.813031	0.266482	2.301442	0.284654
1	122.400	148.650	113.819	0.00968	0.00008	0.00465	0.00696	0.01394	0.06134	0.626	...	0.04368	0.09403	0.01929	19.085	0.458359	0.819521	-4.075192	0.335590	2.486855	0.368674
2	116.682	131.111	111.555	0.01050	0.00009	0.00544	0.00781	0.01633	0.05233	0.482	...	0.03590	0.08270	0.01309	20.651	0.429895	0.825288	-4.443179	0.311173	2.342259	0.332634
3	116.676	137.871	111.366	0.00997	0.00009	0.00502	0.00698	0.01505	0.05492	0.517	...	0.03772	0.08771	0.01353	20.644	0.434969	0.819235	-4.117501	0.334147	2.405554	0.368975
4	116.014	141.781	110.655	0.01284	0.00011	0.00655	0.00908	0.01966	0.06425	0.584	...	0.04465	0.10470	0.01767	19.649	0.417356	0.823484	-3.747787	0.234513	2.332180	0.410335

5 rows × 22 columns

#Create  – Y
Y=df['status']
display (Y.head())

0    1
1    1
2    1
3    1
4    1
Name: status, dtype: int64

#Splitting the data into x_train, y_train, x_test, y_test

X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,random_state=40)
print (X.shape,Y.shape)
print(X_train.shape,X_test.shape,Y_train.shape,Y_test.shape)

(195, 22) (195,)
(156, 22) (39, 22) (156,) (39,)

#Create a Logistic Regression Model
log_reg = LogisticRegression().fit(X_train, Y_train)
#predict on train
train_preds = log_reg.predict(X_train)
#accuracy on train
print("Model accuracy on train is: ", accuracy_score(Y_train, train_preds))

#predict on test
test_preds = log_reg.predict(X_test)
#accuracy on test
print("Model accuracy on test is: ", accuracy_score(Y_test, test_preds))
print('-'*50)

#Confusion matrix
print("confusion_matrix train is:\n ", confusion_matrix(Y_train, train_preds))
print("confusion_matrix test is:\n ", confusion_matrix(Y_test, test_preds))
print('\nClassification Report Train is ')
print(classification_report (Y_train, train_preds))
print('\nClassification Report Test is ')
print(classification_report (Y_test, test_preds))

Model accuracy on train is:  0.8782051282051282
Model accuracy on test is:  0.8717948717948718
--------------------------------------------------
confusion_matrix train is:
  [[ 25  15]
 [  4 112]]
confusion_matrix test is:
  [[ 5  3]
 [ 2 29]]

Classification Report Train is 
              precision    recall  f1-score   support

           0       0.86      0.62      0.72        40
           1       0.88      0.97      0.92       116

    accuracy                           0.88       156
   macro avg       0.87      0.80      0.82       156
weighted avg       0.88      0.88      0.87       156


Classification Report Test is 
              precision    recall  f1-score   support

           0       0.71      0.62      0.67         8
           1       0.91      0.94      0.92        31

    accuracy                           0.87        39
   macro avg       0.81      0.78      0.79        39
weighted avg       0.87      0.87      0.87        39

#Create Random Forest Model

RF=RandomForestClassifier().fit(X_train,Y_train)
#predict on train
train_preds2 = RF.predict(X_train)
#accuracy on train
print("Model accuracy on train is: ", accuracy_score(Y_train, train_preds2))

#predict on test
test_preds2 = RF.predict(X_test)
#accuracy on test
print("Model accuracy on test is: ", accuracy_score(Y_test, test_preds2))

#Confusion matrix
print("confusion_matrix train is:\n ", confusion_matrix(Y_train, train_preds2))
print("confusion_matrix test is:\n ", confusion_matrix(Y_test, test_preds2))
print('\nClassification Report Train is ')
print(classification_report (Y_train, train_preds2))
print('\nClassification Report Test is ')
print(classification_report(Y_test, test_preds2))

#Wrong Predictions made
print((Y_test != test_preds2).sum(), '/', ((Y_test == test_preds2).sum() + (Y_test != test_preds2).sum()))

Model accuracy on train is:  1.0
Model accuracy on test is:  0.8974358974358975
confusion_matrix train is:
  [[ 40   0]
 [  0 116]]
confusion_matrix test is:
  [[ 6  2]
 [ 2 29]]

Classification Report Train is 
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        40
           1       1.00      1.00      1.00       116

    accuracy                           1.00       156
   macro avg       1.00      1.00      1.00       156
weighted avg       1.00      1.00      1.00       156


Classification Report Test is 
              precision    recall  f1-score   support

           0       0.75      0.75      0.75         8
           1       0.94      0.94      0.94        31

    accuracy                           0.90        39
   macro avg       0.84      0.84      0.84        39
weighted avg       0.90      0.90      0.90        39

4 / 39

#Kappa Score
print('KappaScore is: ', metrics.cohen_kappa_score(Y_test,test_preds2))


#Display the test and Predicted Values
ddf=pd.DataFrame(data=[test_preds2,Y_test])
display (ddf)


#Transpose and display
display (ddf.T)


#Decision Tree Classifier
from sklearn.tree import DecisionTreeClassifier
#fit the model on train data
DT = DecisionTreeClassifier().fit(X,Y)


#predict on train
train_preds3 = DT.predict(X_train)
#accuracy on train
print("Model accuracy on train is: ", accuracy_score(Y_train, train_preds3))


#predict on test
test_preds3 = DT.predict(X_test)
#accuracy on test
print("Model accuracy on test is: ", accuracy_score(Y_test, test_preds3))
print('-'*50)
#Confusion matrix
print("confusion_matrix train is:\n ", confusion_matrix(Y_train, train_preds3))
print("confusion_matrix test is: \n", confusion_matrix(Y_test, test_preds3))
print('Wrong predictions out of total')
print('-'*50)
print('\nClassification Report Train is ')
print(classification_report (Y_train, train_preds3))
print('\nClassification Report Test is ')
print(classification_report (Y_test, test_preds3))



#Wrong Prediction and Kappa Score
# Wrong Predictions made.
print((Y_test !=test_preds3).sum(),'/',((Y_test == test_preds3).sum()+(Y_test != test_preds3).sum()))
print('-'*50)

# Kappa Score
print('KappaScore is: ', metrics.cohen_kappa_score(Y_test,test_preds3))

#Naïve Bayce  algorithm

from sklearn.naive_bayes import GaussianNB
#fit the model on train data
NB=GaussianNB()
NB.fit(X_train,Y_train)
#predict on train
train_preds4 = NB.predict(X_train)
#accuracy on train
print("Model accuracy on train is: ", accuracy_score(Y_train, train_preds4))

#predict on test
test_preds4 = NB.predict(X_test)
#accuracy on test
print("Model accuracy on test is: ", accuracy_score(Y_test, test_preds4))
print('-'*50)
#Confusion matrix
print("confusion_matrix train is: \n", confusion_matrix(Y_train, train_preds4))
print("confusion_matrix test is:\n ", confusion_matrix(Y_test, test_preds4))
print('Wrong predictions out of total')
print('-'*50)
print('\nClassification Report Train is ')
print(classification_report (Y_train, train_preds4))
print('\nClassification Report Test is ')
print(classification_report (Y_test, test_preds4))

#Wrong Prediction and Kappa Score
# Wrong Predictions made.
print((Y_test !=test_preds4).sum(),'/',((Y_test == test_preds4).sum()+(Y_test != test_preds4).sum()))
print('-'*50)

# Kappa Score
print('KappaScore is: ', metrics.cohen_kappa_score(Y_test,test_preds4))

#K Neighbours Classifier
from sklearn.neighbors import KNeighborsClassifier
#fit the model on train data
# Using the parameter weights='distance'  to fix the error 'Flags' object has no attribute 'c_contiguous'
KNN = KNeighborsClassifier(weights='distance').fit(X_train,Y_train)
#predict on train
train_preds5 = KNN.predict(X_train)
#accuracy on train
print("Model accuracy on train is: ", accuracy_score(Y_train, train_preds5))

#predict on test
test_preds5 = KNN.predict(X_test)
#accuracy on test
print("Model accuracy on test is: ", accuracy_score(Y_test, test_preds5))
print('-'*50)
#Confusion matrix
print("confusion_matrix train is:\n ", confusion_matrix(Y_train, train_preds5))
print("confusion_matrix test is:\n ", confusion_matrix(Y_test, test_preds5))
print('Wrong predictions out of total')
print('-'*50)
print('\nClassification Report Train is ')
print(classification_report (Y_train, train_preds5))
print('\nClassification Report Test is ')
print(classification_report (Y_test, test_preds5))

#Wrong Prediction and Kappa Score
# Wrong Predictions made.
print((Y_test !=test_preds5).sum(),'/',((Y_test == test_preds5).sum()+(Y_test != test_preds5).sum()))

print('-'*50)
# Kappa Score
print('KappaScore is: ', metrics.cohen_kappa_score(Y_test,test_preds5))

#Support Vector Machine
from sklearn.svm import SVC
#fit the model on train data
SVM = SVC(kernel='linear')
SVM.fit(X_train, Y_train)

#predict on train
train_preds6 = SVM.predict(X_train)
#accuracy on train
print("Model accuracy on train is: ", accuracy_score(Y_train, train_preds6))

#predict on test
test_preds6 = SVM.predict(X_test)
#accuracy on test
print("Model accuracy on test is: ", accuracy_score(Y_test, test_preds6))
print('-'*50)
#Confusion matrix
print("confusion_matrix train is: \n", confusion_matrix(Y_train, train_preds6))
print("confusion_matrix test is:\n ", confusion_matrix(Y_test, test_preds6))
print('Wrong predictions out of total')
print('-'*50)

print("recall", metrics.recall_score(Y_test, test_preds6))
print('-'*50)
print('\nClassification Report Train is ')
print(classification_report (Y_train, train_preds6))
print('\nClassification Report Test is ')
print(classification_report (Y_test, test_preds6))

#Wrong Prediction and Kappa Score
# Wrong Predictions made.
print((Y_test !=test_preds6).sum(),'/',((Y_test == test_preds6).sum()+(Y_test != test_preds6).sum()))
print('-'*50)
# Kappa Score
print('KappaScore is: ', metrics.cohen_kappa_score(Y_test,test_preds6))

#Create Pickle File
import pickle
# Saving model to disk
pickle.dump(SVM,open('deploy_SVM.pkl','wb'))
# Open the Pickle File
model=pickle.load(open('deploy_SVM.pkl','rb'))
# Prediction
print (model.predict (X_train))

KappaScore is:  0.685483870967742

	0	1	2	3	4	5	6	7	8	9	...	29	30	31	32	33	34	35	36	37	38
0	1	1	1	0	1	1	1	1	1	1	...	1	0	0	0	1	1	1	1	0	1
1	1	1	1	0	1	0	1	1	1	0	...	1	0	0	0	1	1	1	1	1	1

2 rows × 39 columns

	0	1
0	1	1
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34	1	1
35	1	1
36	1	1
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Model accuracy on train is:  1.0
Model accuracy on test is:  1.0
--------------------------------------------------
confusion_matrix train is:
  [[ 40   0]
 [  0 116]]
confusion_matrix test is: 
 [[ 8  0]
 [ 0 31]]
Wrong predictions out of total
--------------------------------------------------

Classification Report Train is 
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        40
           1       1.00      1.00      1.00       116

    accuracy                           1.00       156
   macro avg       1.00      1.00      1.00       156
weighted avg       1.00      1.00      1.00       156


Classification Report Test is 
              precision    recall  f1-score   support

           0       1.00      1.00      1.00         8
           1       1.00      1.00      1.00        31

    accuracy                           1.00        39
   macro avg       1.00      1.00      1.00        39
weighted avg       1.00      1.00      1.00        39

0 / 39
--------------------------------------------------
KappaScore is:  1.0
Model accuracy on train is:  0.7307692307692307
Model accuracy on test is:  0.6923076923076923
--------------------------------------------------
confusion_matrix train is: 
 [[38  2]
 [40 76]]
confusion_matrix test is:
  [[ 8  0]
 [12 19]]
Wrong predictions out of total
--------------------------------------------------

Classification Report Train is 
              precision    recall  f1-score   support

           0       0.49      0.95      0.64        40
           1       0.97      0.66      0.78       116

    accuracy                           0.73       156
   macro avg       0.73      0.80      0.71       156
weighted avg       0.85      0.73      0.75       156


Classification Report Test is 
              precision    recall  f1-score   support

           0       0.40      1.00      0.57         8
           1       1.00      0.61      0.76        31

    accuracy                           0.69        39
   macro avg       0.70      0.81      0.67        39
weighted avg       0.88      0.69      0.72        39

12 / 39
--------------------------------------------------
KappaScore is:  0.3937823834196892
Model accuracy on train is:  1.0
Model accuracy on test is:  0.8461538461538461
--------------------------------------------------
confusion_matrix train is:
  [[ 40   0]
 [  0 116]]
confusion_matrix test is:
  [[ 4  4]
 [ 2 29]]
Wrong predictions out of total
--------------------------------------------------

Classification Report Train is 
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        40
           1       1.00      1.00      1.00       116

    accuracy                           1.00       156
   macro avg       1.00      1.00      1.00       156
weighted avg       1.00      1.00      1.00       156


Classification Report Test is 
              precision    recall  f1-score   support

           0       0.67      0.50      0.57         8
           1       0.88      0.94      0.91        31

    accuracy                           0.85        39
   macro avg       0.77      0.72      0.74        39
weighted avg       0.84      0.85      0.84        39

6 / 39
--------------------------------------------------
KappaScore is:  0.48
Model accuracy on train is:  0.8782051282051282
Model accuracy on test is:  0.8974358974358975
--------------------------------------------------
confusion_matrix train is: 
 [[ 23  17]
 [  2 114]]
confusion_matrix test is:
  [[ 5  3]
 [ 1 30]]
Wrong predictions out of total
--------------------------------------------------
recall 0.967741935483871
--------------------------------------------------

Classification Report Train is 
              precision    recall  f1-score   support

           0       0.92      0.57      0.71        40
           1       0.87      0.98      0.92       116

    accuracy                           0.88       156
   macro avg       0.90      0.78      0.82       156
weighted avg       0.88      0.88      0.87       156


Classification Report Test is 
              precision    recall  f1-score   support

           0       0.83      0.62      0.71         8
           1       0.91      0.97      0.94        31

    accuracy                           0.90        39
   macro avg       0.87      0.80      0.83        39
weighted avg       0.89      0.90      0.89        39

4 / 39
--------------------------------------------------
KappaScore is:  0.6533333333333333
[1 1 1 1 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1
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